
distribution - this module contains various checks for estimating if the training and test set have different distributions. This module includes checks such as: comparing the model’s trust score [1]} of the train and test sets;
calculating the drift between the train set and test set per each input feature (in particular Earth Movers Distance for numerical variables and Population Stability Index for nominal features);
calculating the target attribute drift between train set and test set.

integrity - this module contains all data integrity checks, such as: checking for duplicate samples in the dataset; detecting a small amount of a rare data type within a column, such as few string samples in a mostly numeric column; checking if there are columns which have only a single unique value in all rows, etc.

methodology - this module contains checks for methodological flaws in the model building process, such as: Checking for overfitting caused by using too many iterations in a gradient boosted model; Detect features that are nearly unused by the model; Calculating the Predictive Power Scorehttps://github.com/8080labs/ppscore of all features, in order to detect features whose ability to predict the target is due to leakage; Detecting samples in the test data that appear also in training data.

evaluation - Module that contains checks of model performance metrics, such as: Comparing the predictive performance of a given model to that of a relatively simple model (e.g. a single tree model) which is used as a baseline for performance comparison.; Calculating the calibration curve with brier score for each class; Checking the distribution of errors; Finding features that best split the data into segments of high and low model error.

overview - Module that provides meta-information regarding the model and the dataset, such as:
the role and logical type of each column; the model's hyper-parameters, etc.

